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Book of Abstracts for ESGI 188 now available
The 188th European Study Group with Industry (ESGI 188), hosted from May 26 to 30 at Bilbao's B Accelerator Tower (BAT), has concluded with the release of its Book of Abstracts. This event, organized by the Basque Center for Applied Mathematics (BCAM) in collaboration with the Bizkaia Provincial…
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- What if Artificial Intelligence could remember things not just well, but faster or more reliably?
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- The jury of these awards has taken into account her contributions to machine learning in the field of adaptation to temporal changes, both in its mo
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EKIOCEAN Project Concludes Two Years of Research on Sustainable Solutions for Floating Marine Photovoltaics
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View allEfficient Localization via Soft Information With Generic Sensing Measurements
Bartoletti, S.; Mazuelas, S.; Conti, A.; Win, M. (2025-06-05)
Accurate location awareness is essential for various context-based applications. This calls for efficient methodologies to collect, communicate and process position-dependent measurements, especially in situations with limit...
Bourgain’s Counterexample in the Sequential Convergence Problem for the Schrödinger Equation
Cho, C.H.; Eceizabarrena, D. (2025-05-08)
We study the problem of pointwise convergence for the Schrödinger operator on $\mathbb R^n$ along time sequences. We show that the sharp counterexample to the sequential Schrödinger maximal estimate given recently by Li, Wan...
A Unified View of Double-Weighting for Marginal Distribution Shift
Segovia, J.I; Mazuelas, S.; Liu, A. (2025-03-01)
Supervised classification traditionally assumes that training and testing samples are drawn from the same underlying distribution. However, practical scenarios are often affected by distribution shifts, such as covariate and...
Collocation-based robust variational physics-informed neural networks (CRVPINNs)
Paszyński, Maciej; Los, M.; Służalec, T.; Maczuga, P.; Vilkha, A.; Uriarte, C. (2025-09-01)
Physics-informed neural networks (PINNs) have been widely used to solve partial differential equations (PDEs) through strong residual minimization formulations. Their extension to weak scenarios via Variational PINNs (VPINNs...